Chromatin interaction networks and higher order architectures of eukaryotic genomes

Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks

Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available.

CohesinDB: a comprehensive database for decoding cohesin-related epigenomes, 3D genomes and transcriptomes in human cells

Cohesin is a multifunctional protein responsible for transcriptional regulation and chromatin organization. Cohesin binds to chromatin at tens of thousands of distinct sites in a conserved or tissue-specific manner, whereas the function of cohesin varies greatly depending on the epigenetic properties of specific chromatin loci. Cohesin also extensively mediates cis-regulatory modules (CRMs) and chromatin loops. Even though next-generation sequencing technologies have provided a wealth of information on different aspects of cohesin, the integration and exploration of the resultant massive cohesin datasets are not straightforward. Here, we present CohesinDB (https://cohesindb.iqb.u-tokyo.ac.jp), a comprehensive multiomics cohesin database in human cells. CohesinDB includes 2043 epigenomics, transcriptomics and 3D genomics datasets from 530 studies involving 176 cell types. By integrating these large-scale data, CohesinDB summarizes three types of 'cohesin objects': 751 590 cohesin binding sites, 957 868 cohesin-related chromatin loops and 2 229 500 cohesin-related CRMs. Each cohesin object is annotated with locus, cell type, classification, function, 3D genomics and cis-regulatory information. CohesinDB features a user-friendly interface for browsing, searching, analyzing, visualizing and downloading the desired information. CohesinDB contributes a valuable resource for all researchers studying cohesin, epigenomics, transcriptional regulation and chromatin organization.

From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect

The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing technology and application

Background

Long-range chromatin interactions play an important role in transcription regulation. Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET) is an emerging technology that has unique advantages in chromatin interaction analysis, and thus provides insight into the study of transcription regulation.

Results

This article introduces the experimental protocol and data analysis process of ChIA-PET, as well as discusses some applications using this technology. It also unveils the direction of future studies based on this technology.

Conclusions

Overall we show that ChIA-PET is the cornerstone to explore the three-dimensional (3D) chromatin structure, and certainly will lead the forthcoming wave of 3D genomics studies.

Neighboring genes show interchromosomal colocalization after their separation

The order of genes on eukaryotic chromosomes is nonrandom. Some neighboring genes show order conservation among species, while some neighboring genes separate during evolution. Here, we investigated whether neighboring genes show interactions after their separation. We found that neighboring gene pairs tend to show interchromosomal colocalization (i.e., nuclear colocalization) in the species in which they separate. These nuclear colocalized separated neighboring gene pairs 1) show neighborhood conservation in more species, 2) tend to be regulated by the same transcription factor, and 3) tend to be regulated by the same histone modification. These results suggest a mechanism by which neighboring genes could retain nuclear proximity after their separation.

Fractal dimension of chromatin: potential molecular diagnostic applications for cancer prognosis

Fractal characteristics of chromatin, revealed by light or electron microscopy, have been reported during the last 20 years. Fractal features can easily be estimated in digitalized microscopic images and are helpful for diagnosis and prognosis of neoplasias. During carcinogenesis and tumor progression, an increase of the fractal dimension (FD) of stained nuclei has been shown in intraepithelial lesions of the uterine cervix and the anus, oral squamous cell carcinomas or adenocarcinomas of the pancreas. Furthermore, an increased FD of chromatin is an unfavorable prognostic factor in squamous cell carcinomas of the oral cavity and the larynx, melanomas and multiple myelomas. High goodness-of-fit of the regression line of the FD is a favorable prognostic factor in acute leukemias and multiple myelomas. The nucleus has fractal and power-law organization in several different levels, which might in part be interrelated. Some possible relations between modifications of the chromatin organization during carcinogenesis and tumor progression and an increase of the FD of stained chromatin are suggested. Furthermore, increased complexity of the chromatin structure, loss of heterochromatin and a less-perfect self-organization of the nucleus in aggressive neoplasias are discussed.

Down syndrome: the brain in trisomic mode

Down syndrome is the most common form of intellectual disability and results from one of the most complex genetic perturbations that is compatible with survival, trisomy 21. The study of brain dysfunction in this disorder has largely been based on a gene discovery approach, but we are now moving into an era of functional genome exploration, in which the effects of individual genes are being studied alongside the effects of deregulated non-coding genetic elements and epigenetic influences. Also, new data from functional neuroimaging studies are challenging our views of the cognitive phenotypes associated with Down syndrome and their pathophysiological correlates. These advances hold promise for the development of treatments for intellectual disability.

Regional activation of the cancer genome by long-range epigenetic remodeling

Epigenetic gene deregulation in cancer commonly occurs through chromatin repression and promoter hypermethylation of tumor-associated genes. However, the mechanism underpinning epigenetic-based gene activation in carcinogenesis is still poorly understood. Here, we identify a mechanism of domain gene deregulation through coordinated long-range epigenetic activation (LREA) of regions that typically span 1 Mb and harbor key oncogenes, microRNAs, and cancer biomarker genes. Gene promoters within LREA domains are characterized by a gain of active chromatin marks and a loss of repressive marks. Notably, although promoter hypomethylation is uncommon, we show that extensive DNA hypermethylation of CpG islands or "CpG-island borders" is strongly related to cancer-specific gene activation or differential promoter usage. These findings have wide ramifications for cancer diagnosis, progression, and epigenetic-based gene therapies.

Large-scale functional organization of long-range chromatin interaction networks

Chromatin interactions play important roles in transcription regulation. To better understand the underlying evolutionary and functional constraints of these interactions, we implemented a systems approach to examine RNA polymerase-II-associated chromatin interactions in human cells. We found that 40% of the total genomic elements involved in chromatin interactions converged to a giant, scale-free-like, hierarchical network organized into chromatin communities. The communities were enriched in specific functions and were syntenic through evolution. Disease-associated SNPs from genome-wide association studies were enriched among the nodes with fewer interactions, implying their selection against deleterious interactions by limiting the total number of interactions, a model that we further reconciled using somatic and germline cancer mutation data. The hubs lacked disease-associated SNPs, constituted a nonrandomly interconnected core of key cellular functions, and exhibited lethality in mouse mutants, supporting an evolutionary selection that favored the nonrandom spatial clustering of the least-evolving key genomic domains against random genetic or transcriptional errors in the genome. Altogether, our analyses reveal a systems-level evolutionary framework that shapes functionally compartmentalized and error-tolerant transcriptional regulation of human genome in three dimensions.